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4 Python Tools Every Data Scientist Should Start Using

Pandas GUI — a graphical interface for Pandas

Pandas GUI abstracts pandas into a really user friendly environment that makes exploratory data analysis and data wrangling much more visual for the user.

Here is a demo video:

To install, use this line of code:

pip install pandasgui

Within Pandas GUI, you can filter datasets, generate summary statistics and more!

Mito — A Spreadsheet Extension for Jupyter Lab that Generates Code

Mito is an editable spreadsheet that you can call into your Python environment. Each edit you make in the Mitosheet will generate the equivalent Python in the code cell below. Mito is powerful for data scientists who want to get their analysis done faster, or Excel-familiar users, who want to generate their Python in a similar environment.

Here is a demo video for Mito:

Within Mito, you can:

  • Create Visualizations
  • Look at Summary Statistics
  • Merge Dataset Together
  • Generate Pivot Tables
  • Edit Specific Cell Values
  • Filter and Sort Datasets
  • Save and Replay Analyses (macros)
  • See Your Edit Steps
  • and more…
from Author

Each edit generates the equivalent Python.

From Author

To install Mito, run these commands:

python -m pip install mitoinstaller
python -m mitoinstaller install

Then open Jupyter Lab and render a Mitosheet:

import mitosheet
mitosheet.sheet()

Here are the full install instructions.

Lux — Automated Visualization Suggestions and Generation

Lux will take any data frame and automatically suggest visualizations that the user can click on and create in an instant. Getting the syntax correct for visualizations can be time consuming and Lux reduces this time to only a few seconds.

To install lux:

https://github.com/lux-org/lux

In the above gif, you can see how the user can pass in any dataframe and Lux will bring up a menu of charts to choose from. The focus of these charts range from data exploration to presentation. The user can also download these charts to share further.

Lux also offer an intent feature, where you can specify which attributes (columns) they are interested in. Lux will offer charts specific to those columns.

df.intent = ["Column1","Column2"]
df
https://github.com/lux-org/lux

Bokeh — Create Interactive Visualizations in Python

To start using Bokeh:

import numpy as np from bokeh.io import output_notebook, show
from bokeh.plotting import figure

Bokeh innovates on graphing packages like Matplotlib and Seaborn by creating plots that are interactive for end users.

Here are some of the options they offer:

https://demo.bokeh.org/

Here is the full documentation

Interactive charts are becoming increasingly import in data science. Many packages, like Plotly and Streamlit, are working to give data scientist easy ways to make their work valuable to users outside the data science team. Bokeh seems to be working in that same direction.

Here is a demo video from the Data Professor that walks through great graphing packages like Bokeh:

I hope you find these tools helpful :)

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Jake from Mito

Jake from Mito

Exploring the future of Python and Spreadsheets